We investigate the modification of the intrinsic carrier noise spectral density induced in low-doped semiconductor materials by an external correlated noise source added to the driving high-frequency periodic electric field. A Monte Carlo approach is adopted to numerically solve the transport equation by considering all the possible scattering phenomena of the hot electrons in the medium. We show that the noise spectra are strongly affected by the intensity and the correlation time of the external random electric field. Moreover, this random field can cause a suppression of the total noise power.
A Monte Carlo study of the role of the frequency on the hot-electron intrinsic noise reduction in an n-type GaAs bulk driven by two mixed cyclostationary electric fields is presented. Previous numerical results showed the possibility to reduce the diffusion noise under specific wave-mixing conditions. In this work the variations of the noise properties are investigated by computing and integrating the spectral density of the velocity fluctuations. We found that the effect of reduction of the noise level due to the addition of a second field at twice frequency is almost independent of the frequency.
Diffusion of potassium adsorbate on the W(112) plane with preadsorbed nickel was investigated by means of the autocorrelation function of field emission current fluctuations. A comparison of the experimental autocorrelation function with that of a theoretical calculation by Gesley and Swanson gives the surface diffusion coefficient of potassium. From its temperature dependence the diffusion activation energy at several Ni pre-coverages for a constant potassium coverage (Θ_K=0.4) is derived. The dependence of the activation energy for potassium surface diffusion on the nickel coverage - first reduction and then an increase with increasing Ni coverage - is observed. This is in agreement with the results obtained from the spectral analysis of the field emission current fluctuations of the same system. The decrease is understood to be a result of the smoothing effect caused by Ni atoms on the W(112) plane.
This work studies the stability and the stochastic properties of neural activity evoked by external stimulation. The underlying nonlocal model describes the spatiotemporal response dynamics of neural populations involving both synaptic delay and axonal transmission delay. We show that the linear model recasts to a set of affine delay differential equations in spatial Fourier space. Besides a stability study for general kernels and general external stimulation, the power spectrum of evoked activity is derived analytically in the case of external Gaussian noise. Further applications to specific kernels reveal critical fluctuations at Hopf- and Turing bifurcations and allow the numerical detection of 1/f^αfluctuations near the stability threshold.
The proposed article presents a new approach to analyze the relationships between financial instruments. We use blind signal separation methods to decompose time series into the core components. The components common to the various instruments provide broad set of characteristics to describe the internal morphology of the time series. In this research a modified and extended version of AMUSE algorithm is used. The concept is presented based on real financial instruments.
Si and GaAs avalanche diodes containing microplasmas are investigated. Microwave field applied to the diode in addition to reverse dc bias results in considerable spread of noise spectrum and in the increase of noise power. The microplasma noise spectra cover very high (30 to 300 MHz) and ultrahigh (300 to 1000 MHz) frequency bands, while the effective noise temperature is about 10^8 K.
In this work we analyze empirically customer churn problem from a physical point of view to provide objective, data driven and significant answers to support decision making process in business application. In particular, we explore different entropy measures applied to decision trees and assess their performance from the business perspective using set of model quality measures often used in business practice. Additionally, the decision trees are compared with logistic regression and two machine learning methods - neural networks and support vector machines.
A generalized algorithm for building classification trees, based on Tsallis q-entropy, is proposed and applied to classification of Polish households with respect to their incomes. Data for 2008 are used. Quality measures for obtained trees are compared for different values of q parameter. A method of choosing the optimum tree is elaborated.
In this paper we present a novel similarity measure method for financial data. In our approach, we propose the assessment of the similarity in a coherent hierarchical and multi-faceted way, following the general scheme where various detailed basic measures may be used like the Fermi-Dirac divergence, Bose-Einstein divergence, or our new smoothness measure. The presented method is tested on benchmark and real stock markets data.
The article presents independent component analysis (ICA) applied to the concept of ensemble predictors. The use of ICA decomposition enables to extract components with particular statistical properties that can be interpreted as destructive or constructive for the prediction. Such process can be treated as noise filtration from multivariate observation data, in which observed data consist prediction results. As a consequence of the ICA multivariate approach, the final results are combination of the primary models, what can be interpreted as aggregation step. The key issue of the presented method is the identification of noise components. For this purpose, a new method for evaluating the randomness of the signals was developed. The experimental results show that presented approach is effective for ensemble prediction taking into account different prediction criteria and even small set of models.
Surface diffusion of palladium,Θ=2 ML, on a tantalum microcrystal was studied by means of field electron emission microscopy within the temperature range 665-790 K. The observed sharp moving boundary-diffusion proceeds with an activation energy ranging from 1.25 to 1.7 eV/atom, depending on the crystallographic direction.
Scattering from the very simple ring graph is shown to display several basic features which underlie the complex (chaotic) phenomena observed in scattering from more complex graphs. In particular we demonstrate the appearance of arbitrarily narrow resonances - the "topological resonances" which are directly linked to the existence of cycles. We use the ring graph to study the response of such resonances to perturbations induced by a time-dependent random noise.
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